GPT-5.2 vs Muse Ember
Muse Ember leads GPT-5.2 on coding, 64.9 to 56.1 on the Vibe Coding Index, and Muse Ember costs 28.0x less per output token ($0.50 vs $14.00 per million). Updated November 14, 2026; 3 benchmarks have a result for both.
vs boards won of 3 shared
GPT-5.2 or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 56.1 for GPT-5.2.
GPT-5.2 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Model AGPT-5.2OpenAI56.1/1003 independent benchmarks$1.91 per solved taskModel BMuse EmberMeta64.9/1003 independent benchmarksNo cost per solved task
- Shared results
- 3
- GPT-5.2 only
- 112
- Muse Ember only
- 12
- Like-for-like categories
- 1 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
Muse Ember
Muse Ember scores 64.9 against 56.1.
3 benchmarks behind each score
Reasoning
GPQA Diamond
No call
Only GPT-5.2 has a GPQA Diamond result, 91.7%.
Directional only
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.2 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.2 has a Design Arena: websites result, 1186.
Directional only
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
Muse Ember
Muse Ember takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
GPT-5.2 $0.0088, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- GPT-5.2 56.1 from 3 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Coding, 28.4 points apart on average over 3 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.2 53.5% against Muse Ember 83.7%, 30.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Muse Ember $0.50 per million output tokens, 28.0x apart.
- Context window
- GPT-5.2 takes 400K tokens, Muse Ember 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.2 | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| All shared boards | 0 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2 measured |
| SWE-bench Multilingual | 66.7% | Not measured | Only GPT-5.2 measured |
| LMArena WebDev | 1421 | 1609 | Muse Ember |
| LiveCodeBench (Vals AI) | 85.4% | Not measured | Only GPT-5.2 measured |
| SWE-bench Verified (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 39.4% | Only Muse Ember measured |
| Vibe Code Bench (Vals AI) | 53.5% | 83.7% | Muse Ember |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | Not measured | 40.9% | Only Muse Ember measured |
| FrontierCode 2.0 | Not measured | 27.2% | Only Muse Ember measured |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 measured |
| GSO-Bench | 27.5% | Not measured | Only GPT-5.2 measured |
| WeirdML | 72.2% | Not measured | Only GPT-5.2 measured |
| MirrorCode | 32.2% | Not measured | Only GPT-5.2 measured |
| ALE-Bench | 1293.5 | Not measured | Only GPT-5.2 measured |
| LMArena Text: coding | 1441 | Not measured | Only GPT-5.2 measured |
| LMArena Text: software and IT | 1434 | Not measured | Only GPT-5.2 measured |
| LMArena WebDev: React | Not measured | 1616 | Only Muse Ember measured |
| LMArena WebDev: HTML | 1431 | 1621 | Muse Ember |
| Terminal-Bench 2.0 | 64.9% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| Epoch Capabilities Index | 154.5 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1062 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | 1017 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | 1085 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1045 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| Tax Agent Bench (Vals AI) | Not measured | 38.2% | Only Muse Ember measured |
| Legal Research Bench (Vals AI) | Not measured | 49.0% | Only Muse Ember measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember measured |
| τ²-Bench Telecom | 84.8% | Not measured | Only GPT-5.2 measured |
| APEX-Agents | Not measured | 64.3% | Only Muse Ember measured |
| METR time horizons | 75.3% | Not measured | Only GPT-5.2 measured |
| Remote Labor Index | 2.5% | Not measured | Only GPT-5.2 measured |
| GDPval | 49.7% | Not measured | Only GPT-5.2 measured |
| DeepResearch Bench | 41.1% | Not measured | Only GPT-5.2 measured |
| Vending-Bench 2 | $3,591 | Not measured | Only GPT-5.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 55.9% | Not measured | Only GPT-5.2 measured |
| MCP Atlas | 67.6% | Not measured | Only GPT-5.2 measured |
| τ³-Banking | 32.2% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.2 measured |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | Not measured | Only GPT-5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | Not measured | Only GPT-5.2 measured |
| HLE-Diamond (Artificial Analysis run) | Not measured | 25.1% | Only Muse Ember measured |
| CritPt v2, hard subset (Artificial Analysis run) | Not measured | 39.0% | Only Muse Ember measured |
| GPQA Diamond (Epoch AI run) | 91.4% | Not measured | Only GPT-5.2 measured |
| ARC-AGI | 86.2% | Not measured | Only GPT-5.2 measured |
| ARC-AGI-2 | 52.9% | Not measured | Only GPT-5.2 measured |
| SimpleBench | 45.8% | Not measured | Only GPT-5.2 measured |
| Chess puzzles | 48.0% | Not measured | Only GPT-5.2 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5.2 measured |
| EBR-bench | 23.0% | Not measured | Only GPT-5.2 measured |
| VPCT | 84.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1428 | Not measured | Only GPT-5.2 measured |
| LMArena Text: expert | 1444 | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts in English | 1431 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: diagrams | 1265 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: homework | 1276 | Not measured | Only GPT-5.2 measured |
| Kagi LLM Benchmark | 73.3% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| AIME | 96.9% | Not measured | Only GPT-5.2 measured |
| MGSM (Vals AI) | 94.0% | Not measured | Only GPT-5.2 measured |
| AIME 2025 (Artificial Analysis run) | 99.0% | Not measured | Only GPT-5.2 measured |
| OTIS Mock AIME | 96.1% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tier 4 | 31.7% | Not measured | Only GPT-5.2 measured |
| ProofBench | 15.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: math | 1440 | Not measured | Only GPT-5.2 measured |
| LMArena Text: mathematical work | 1442 | Not measured | Only GPT-5.2 measured |
| MathArena | 31.2% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | Not measured | Only GPT-5.2 measured |
| LegalBench (Vals AI) | 82.8% | Not measured | Only GPT-5.2 measured |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | Not measured | Only GPT-5.2 measured |
| TaxEval (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| MortgageTax (Vals AI) | 67.1% | Not measured | Only GPT-5.2 measured |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | Not measured | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| AA-Omniscience | Not measured | 7.7 | Only Muse Ember measured |
| SimpleQA Verified | 37.1% | Not measured | Only GPT-5.2 measured |
| LMArena Text: science | 1425 | Not measured | Only GPT-5.2 measured |
| LMArena Text: medicine and healthcare | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: legal and government | 1428 | Not measured | Only GPT-5.2 measured |
| LMArena Text: business and finance | 1414 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: websites | 1186 | Not measured | Only GPT-5.2 measured |
| Design Arena: UI components | 1180 | Not measured | Only GPT-5.2 measured |
| Design Arena: game dev | 1184 | Not measured | Only GPT-5.2 measured |
| Design Arena: data viz | 1202 | Not measured | Only GPT-5.2 measured |
| Design Arena: 3D | 1058 | Not measured | Only GPT-5.2 measured |
| Design Arena: all code categories | 1159 | Not measured | Only GPT-5.2 measured |
| Design Arena: SVG | 1128 | Not measured | Only GPT-5.2 measured |
| Design Arena: ASCII art | 1147 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index34 boards, none shared
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.2 measured |
| IFBench | 75.4% | Not measured | Only GPT-5.2 measured |
| Long context reasoning (AA-LCR) | 82.7% | Not measured | Only GPT-5.2 measured |
| DTBench | 90.9% | Not measured | Only GPT-5.2 measured |
| LMCA | 43.9% | Not measured | Only GPT-5.2 measured |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | Not measured | Only GPT-5.2 measured |
| LMArena Text: instruction following | 1409 | Not measured | Only GPT-5.2 measured |
| LMArena Text: creative writing | 1376 | Not measured | Only GPT-5.2 measured |
| LMArena Vision | 1245 | Not measured | Only GPT-5.2 measured |
| LMArena Text: English | 1422 | Not measured | Only GPT-5.2 measured |
| LMArena Text: non-English | 1405 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Chinese | 1448 | Not measured | Only GPT-5.2 measured |
| LMArena Text: German | 1442 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Japanese | 1418 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Korean | 1375 | Not measured | Only GPT-5.2 measured |
| LMArena Text: French | 1454 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Spanish | 1412 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Russian | 1416 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Polish | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: multi-turn | 1419 | Not measured | Only GPT-5.2 measured |
| LMArena Text: longer queries | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: writing and language | 1392 | Not measured | Only GPT-5.2 measured |
| LMArena Text: entertainment and media | 1378 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: English | 1243 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: Chinese | 1305 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: reading text | 1256 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: humor | 1228 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: captioning | 1266 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1225 | Not measured | Only GPT-5.2 measured |
| LMArena Document | 1404 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-5.2$0.0088Fits in one request
Muse Ember$0.0003Fits in one request
Muse Ember costs less
Repository review
50K input + 3K output tokens
GPT-5.2$0.130Fits in one request
Muse Ember$0.0065Fits in one request
Muse Ember costs less
Agent loop
200K input + 10K output tokens
GPT-5.2$0.490Fits in one request
Muse Ember$0.025Fits in one request
Muse Ember costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.2 | Meta Muse Ember |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | No public weights listed |
| Released | December 10, 2025 | November 9, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.10 |
| Output $/Mtok | $14.00 | $0.50 |
| Coding index | 56.1 | 64.9 |
| Independent measurements behind it | 3 | 3 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 56.1 for GPT-5.2. GPT-5.2 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, GPT-5.2 or Muse Ember?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 or Muse Ember?
Muse Ember, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.2 vs Muse Ember comparison based on?
3 benchmarks have a result for both models as of November 14, 2026. GPT-5.2's coding index rests on 3 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.